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Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Atomic Force Microscopy01:08

Atomic Force Microscopy

Atomic force microscopy (AFM) is a type of scanning probe microscopy that can analyze topographic details of various specimens like ceramics, glass, polymers, and biological samples. AFM offers over 1000 times more resolution than the optical imaging system. Images generated from AFM are three-dimensional surface profiles, offering an advantage over the flat, two-dimensional images from other imaging techniques.
The AFM Probe
The probe is regarded as the heart of any AFM setup and comprises the...

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相关实验视频

Updated: Jun 19, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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ART-SM:通过机器学习增强基于碎片的后映射.

Christian Pfaendner1,2, Viktoria Korn1, Pritom Gogoi1

  • 1Stuttgart Center for Simulation Science, Cluster of Excellence EXC 2075, University of Stuttgart, Universitätsstr. 32, 70569 Stuttgart, Germany.

Journal of chemical theory and computation
|April 4, 2025
PubMed
概括
此摘要是机器生成的。

ART-SM是一个新的框架,用于在分子动力学模拟中进行无的逆向映射. 它准确地从粗粒度模型中重建原子细节,使复杂系统的高效模拟成为可能.

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科学领域:

  • 计算化学的计算化学
  • 分子动力学模拟模型
  • 生物物理学的生物物理.

背景情况:

  • 连续的多尺度分子动力学模拟结合了粗粒度 (CG) 和原子化 (AT) 的分辨率.
  • 后映射,即从CG模型中重新引入AT细节的过程,是一个复杂的挑战.

研究的目的:

  • 介绍ART-SM,一种基于片段的新型背景绘图框架.
  • 在分子动力学中实现从CG到AT分辨率的无过渡.

主要方法:

  • ART-SM使用基于碎片的方法与机器学习来选择最佳的形状.
  • 在反向映射过程中,一个新的精细化步骤优化了债券,角度和二面角.
  • 该框架是通过原子模拟数据进行训练的.

主要成果:

  • ART-SM准确地恢复了小分子和线性分子的原子分布.
  • 对TIP3P水模型的重建是快速而稳健的.
  • 使用Martini3力场,成功地对复杂表面活性剂小粒 (TAPB和SDS) 进行了逆向映射.

结论:

  • ART-SM提供了一种准确而高效的逆向映射方法.
  • 该框架显示了对更大,更复杂的分子 (如脂质和蛋白质) 的应用潜力.